How do I get Degreed API credentials?

Log in to your Degreed admin account, navigate to the API settings section, and create a new application to retrieve your Client ID and Client Secret for OAuth 2.0 authentication.

Can the agent assign learning content?

This integration currently focuses on discovering content and monitoring progress. Assigning content to specific users is managed via the Degreed dashboard or mobile app.

Are skill ratings included?

Yes, when retrieving user profiles, the agent can provide information on skills associated with the user and their current proficiency levels where available.

Degreed MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Degreed
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Degreed through Vinkius, pass the Edge URL in the `mcps` parameter and every Degreed tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Degreed Specialist",
    goal="Help users interact with Degreed effectively",
    backstory=(
        "You are an expert at leveraging Degreed tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Degreed "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Degreed MCP Server

Integrate Degreed, the leading upskilling and learning experience platform (LXP), directly into your AI workflow. Discover available learning content, monitor employee skill profiles, and track progress across pathways and plans using natural language.

When paired with CrewAI, Degreed becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Degreed tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Content Discovery — Search the entire Degreed catalog for courses, articles, and videos matching specific keywords.
Skill Intelligence — List and review the defined skills taxonomy and individual user skill profiles.
Learning Oversight — Monitor user completions, active learning plans, and curated pathways.
User Research — Retrieve detailed metadata and activity summaries for learners in your organization.

The Degreed MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Degreed to CrewAI via MCP

Follow these steps to integrate the Degreed MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Degreed

Why Use CrewAI with the Degreed MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Degreed through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Degreed + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Degreed MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Degreed for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Degreed, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Degreed tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Degreed against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Degreed MCP Tools for CrewAI (10)

These 10 tools become available when you connect Degreed to CrewAI via MCP:

get_content_details

Resolves detailed descriptions, associated skill tags, and duration metadata. Get detailed metadata for a specific learning item

get_user_profile

Resolves assigned skill ratings, learning progress, and active pathways within the Degreed ecosystem. Get full profile and skill data for a specific user

list_active_learners

Identifies users with recent completion activity within the Degreed workspace. List users who have completed learning recently

list_defined_skills

Returns the standardized list of skills and competencies defined by the organization for talent mapping. List the skills taxonomy defined in your organization

list_degreed_users

Returns a list of users with metadata including system IDs, professional titles, and organizational affiliations. List all users registered in your Degreed organization

list_learning_content

Returns content metadata including titles, providers, content types (e.g., article, video, course), and external URLs. List all available learning content in the Degreed catalog

list_learning_pathways

Returns pathway metadata including objectives, total duration, and completion requirements. List curated learning pathways available to users

list_learning_plans

Returns active learning plans, including target completion dates and linked competencies. List learning plans and goals configured in the system

list_user_completions

Returns a history of all learned items with completion timestamps and earned skill points. List all learning content completed by a specific user

search_learning_catalog

Matches terms against titles, descriptions, and skill tags to return a ranked list of relevant learning materials. Search for learning content by keyword or term

Example Prompts for Degreed in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Degreed immediately.

"Search for courses related to 'Data Science with Python'."

"List all learning plans for user 'Alice Johnson'."

"What skills are most common in the 'Engineering' team?"

Troubleshooting Degreed MCP Server with CrewAI

Common issues when connecting Degreed to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Degreed + CrewAI FAQ

Common questions about integrating Degreed MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.